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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2603.29740 |
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| _version_ | 1866912994577874944 |
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| author | Minenna, Damien F. G. Dilasser, Guillaume Penavaire, Robin Calvelli, Valerio de Chabannes, Thibault Lecrevisse, Thibault Achard, Thomas Coz, Jason Le Berriaud, Christophe Bolzon, Benoît Caunes, Antomne Fazilleau, Phillipe Felice, Hélène Genot, Clément Guinet, Antoine Jerance, Nikola Juster, François-Paul Lemercier, Thibaut Lenoir, Gilles Lorin, Clément Perron, Yann Pucheu-Plante, Camille Rochepault, Étienne Simon, Damien Stacchi, Francesco Segreti, Michel Trauchessec, Vincent Tuske, Olivier Zgour, Hajar |
| author_facet | Minenna, Damien F. G. Dilasser, Guillaume Penavaire, Robin Calvelli, Valerio de Chabannes, Thibault Lecrevisse, Thibault Achard, Thomas Coz, Jason Le Berriaud, Christophe Bolzon, Benoît Caunes, Antomne Fazilleau, Phillipe Felice, Hélène Genot, Clément Guinet, Antoine Jerance, Nikola Juster, François-Paul Lemercier, Thibaut Lenoir, Gilles Lorin, Clément Perron, Yann Pucheu-Plante, Camille Rochepault, Étienne Simon, Damien Stacchi, Francesco Segreti, Michel Trauchessec, Vincent Tuske, Olivier Zgour, Hajar |
| contents | Superconducting magnets for particle accelerators are particularly challenging to design because they involve a large number of coupled physical phenomena and the management of complex datasets. Artificial Intelligence (AI), including machine learning and advanced optimisation techniques, offers promising approaches to address these challenges and accelerate the design process. This paper presents a new AI-based optimisation and data management platform, and highlights several ongoing applications of AI methods carried out at CEA Paris-Saclay, including multiphysics optimisation using active learning, topology optimisation, holistic modelling of an Electron Cyclotron Resonance (ERC) ion source, and anomaly detection in quench events. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29740 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-Driven Optimisation of Superconducting Magnets at CEA Paris-Saclay Minenna, Damien F. G. Dilasser, Guillaume Penavaire, Robin Calvelli, Valerio de Chabannes, Thibault Lecrevisse, Thibault Achard, Thomas Coz, Jason Le Berriaud, Christophe Bolzon, Benoît Caunes, Antomne Fazilleau, Phillipe Felice, Hélène Genot, Clément Guinet, Antoine Jerance, Nikola Juster, François-Paul Lemercier, Thibaut Lenoir, Gilles Lorin, Clément Perron, Yann Pucheu-Plante, Camille Rochepault, Étienne Simon, Damien Stacchi, Francesco Segreti, Michel Trauchessec, Vincent Tuske, Olivier Zgour, Hajar Accelerator Physics Plasma Physics Superconducting magnets for particle accelerators are particularly challenging to design because they involve a large number of coupled physical phenomena and the management of complex datasets. Artificial Intelligence (AI), including machine learning and advanced optimisation techniques, offers promising approaches to address these challenges and accelerate the design process. This paper presents a new AI-based optimisation and data management platform, and highlights several ongoing applications of AI methods carried out at CEA Paris-Saclay, including multiphysics optimisation using active learning, topology optimisation, holistic modelling of an Electron Cyclotron Resonance (ERC) ion source, and anomaly detection in quench events. |
| title | Data-Driven Optimisation of Superconducting Magnets at CEA Paris-Saclay |
| topic | Accelerator Physics Plasma Physics |
| url | https://arxiv.org/abs/2603.29740 |